Kabir Uddin Ahmed's presentation at UNICEF Innocenti's Inception Scoping Workshop for Evidence on Educational Strategies to Address Child Labour in India & Bangladesh, held in New Delhi in November 2019.

1. Patterns & Trends in Child Labour &
Schooling & their Intersections in Bangladesh
Kabir Uddin Ahmed
Bangladesh Bureau of Statistics
Inception Scoping Workshop: Evidence on Educational
Strategies to Address Child Labour in India & Bangladesh
13-14 Nov 2019, New Delhi, India

2. Focus
Children and adolescents aged 5 to 17 years
Children in economic activities, child labour, and hazardous child labour
School attendance and explanations of educational discontinuity

3. 8.3
15.0
4.1
11.4
34.5
4.3
8.6
16.0
4.6
9.6
33.5
4.3
-40.0 -30.0 -20.0 -10.0 0.0 10.0 20.0 30.0 40.0
0-4
5-14
15-17
18-24
25-64
65+
Female Male
Population characteristics
(in million) by age group and sex
Total population: 154.15 million, 76.6m (49.4%) male, 77.5m (50.6%) female (Child Labour Survey, 2013)
Distribution of very young population, aged 5-17 years (39.2 million), high compared to 65+

4. Distribution of child population (5-17 years)
in million by administrative division, locality, and sex
Division Rural Urban Boy Girl Total
Barisal 1.7 0.5 1.1 1.0 2.1
Chittagong 5.8 2.4 4.2 4.0 8.2
Dhaka 8.5 4.8 6.8 6.6 13.4
Khulna 3.2 0.9 2.2 1.9 4.2
Rajshahi 3.9 1.0 2.7 2.3 5.0
Rangpur 3.7 0.8 2.4 2.1 4.5
Sylhet 2.0 0.4 1.2 1.2 2.4
Total 28.8 10.8 20.6 19.1 39.7
25.7 % of total population were children aged 5-17 years. Of them, 51.9% boys and 48.15 girls

5. Trends of child labour in Bangladesh (2002 to 2013)
Indicator
2002 2013
Total Boys Girls Total Boys Girls
Working children (000) 7423 5471 1952 3450 2103 1348
Working children but not child labour (000) 4244 3010 1234 1751 1150 602
Child labour (000) 3179 2461 718 1699 953 746
Hazardous child labour (000) 1291 1172 120 1280 772 508
Child labour as % of working children 42.8 45 36.8 49.3 45.3 55.4
Child labour as % of total children 7.5 10.8 3.6 4.3 4.6 3.9
Working children in informal employment (%) 92.9 92.7 93.6 94.9 96.4 92.5
Working children reduced by 53.5%
Child labour reduced by 46.6%
Hazardous child labour remain same

6. Children not in employment
(mi) 36.20 million, 91%
Children in employment
(mi), 3.45 million 9%
What do the
39.65 million
children do?

7. Total
Children
39.65 m
Working
children
3.45m
Child labour
1.69m
Hazardous
child labour
1.28m
8.7% of total is Working Children
4.3% of total is Child Labour
3.2% of total is Hazardous Child Labour
Children’s engagement in economic activities

8. Total
Children
20.59 m
Working
children
2.1m
Child
labour
0.95m
Hazardou
s child
labour
0.77m
Children’s engagement in economic activities by sex
Total
Children
19.05 m
Working
children
1.4m
Child
labour
0.75m
Hazardou
s child
labour
0.51m
Boys aged 5-17 Girls aged 5-17

9. 89.3% of working children in
informal employment
Proportion of informal
employment higher for girls
(96.2%) than boys (84.9%)
84.9
96.2
89.3
15.1
3.8
10.7
0
20
40
60
80
100
120
Boy Girl Total
Informal Formal
Working Children by Sector
(Informal / Formal) and sex

10. Varies by locality
As high as 12.8% for the City
corporation area
Lowest 8.5% in Urban areas
City corporation treated as
Urban area
9.7
8.5
12.8
9.7
Rural
Urban
City corporation
Bangladesh
Participation rate of children by locality

11. Varies widely by age-group
Slightly higher in Urban areas
1.4
7.1
28.1
1.6
9.8
25.7
1.4
7.8
27.5
5-9 10-14 15-17 5-9 10-14 15-17 5-9 10-14 15-17
RURAL URBAN TOTAL
Participation rate of children by locality & age group

12. 3.5
40.0
9.7
Attending school Not attending
school
Total
Varies very widely based on
school going status
As high as 40.0% for children
not attending school
Only 3.5% for the school
going children
Participation rate of children by school going status

13. 7.0
11.6
11.4
6.5
6.8
8.2
11.4
9.7
Barisal
Chittagong
Dhaka
Khulna
Rajshahi
Rongpur
Sylhet
Bangladesh
Varied widely by geographic
region
Higher in industrialized
regions i.e. Dhaka and
Chittagong
Participation rate of children by geographic region

14. 43.9% of total (1.69 million) were girls
71.1% aged 14-17 years
More girls (34.2%) than boys (20.6%) work in age group 5-11
Age
group
Number % of total child labour Column %
Boy Girl Total Boy Girl Total Boy Girl Total
5 3395 15925 19320 0.2 0.9 1.1 0.4 2.1 1.1
6 –11 192988 239200 432188 11.4 14.1 25.4 20.2 32.1 25.4
12–13 24803 13963 38766 1.5 0.8 2.3 2.6 1.9 2.3
14–17 732018 476602 1208620 43.1 28.1 71.1 76.8 63.9 71.1
Total 953204 745690 1698894 56.1 43.9 100.0 100.0 100.0 100.0
Child Labour in Bangladesh, 2013

15. Boy, 13.6
Boy, 23.3
Boy, 19.2
Girl, 16.3
Girl, 17.5
Girl, 10.1
Agriculture Industry Service
Girls outnumber Boys in
Agriculture
Boys dominating in Industry and
Service sectors.
41% of child labour is in Industry
sector
30% of child labour is in
Agriculture sector
Child labour by broad economic sectors (%)

16. 29.9%
33.3%
6.9%
10.6%
5%
Agriculture, forestry and fishing
Mining and quarrying
Construction
Wholesale and retail trade, repair of
motor vehicles and motorcycles
Transportation and storage
Totalchildlabour:1.69million
Two-thirds (63.2%) of child labour in Agriculture and Mining and quarrying sectors
Distribution of Child labour by major sub-sectors

17. 79.5
10.0 10.6
30.9
61.1
8.0
28.6
63.0
8.4
19.2
73.1
7.7
Currently attending Currently not attending Never attended school
Total children
Working Children
Child labour
Hazardous Child Labour
School attending status showed decreasing trends: from Total (79.5%) to Working
Children (30.9%); to Child labour (28.6%), to lowest (19.2%) for Hazardous Child Labour
Working Children and school going status

18. 4.2 million (10.6%) of children
reported they never attended
school
Among them, 8% are involved in
an economic activity, 8.4% in
child labour, and 7.7 % involved in
hazardous work
Total
Children
10%
Working
children
8%
Child labour
8.4%
Hazardous
child labour
7.7%
Employment characteristics of children who never
attended school

19. Causes why never attended school Boy Girl Total
School too far 0.1 1.6 0.7
To support family income 3.5 0.6 2.3
Education not necessary 2.7 2.7 2.7
To do domestic chores 0.2 42.6 18.0
Parents did not want 0.3 7.4 3.3
Cannot afford expense 64.3 18.8 45.2
Others 28.9 26.3 27.8
Total 100.0 100.0 100.0
Factors hindering school attendance
(who never attended school)

20. Reasons for drop out Boy Girl Total
Failed examination 4.8 4.8 4.8
Not interested in education 8.3 22.4 13.5
To start working 14.7 8.7 12.5
To get married 0 7.5 2.7
To support family income 20.3 11.6 17.1
Parents did not want 2.2 7.4 4.1
No school nearby 9.6 1.3 6.6
Could not afford education 39.5 34.8 37.8
Others 0.7 1.4 0.9
Total 100 100 100
Working children by reasons for being dropped out

21. 18.1
26.8
53.7
1.16.7
41.5 42.4
9.2
13.7
32.5
49.3
4.3
Own account worker Contributing family
member
Employee Others
Boy
Girl
Total
13.7% reported as Own account workers (6.7% girl)
32.5% as contributing family member (41.5% girl)
49.3% of them represent employee/hired labour (42.1% girl)
Working children by status of employment & sex

22. Data are not comparable: Child Labour Survey conducted in 1996, 2002-03, 2013.
Definitions were not consistent/unique, estimates are not comparable
Data needs to be generated to provide SDG indicator: After 2013, no exclusive survey
on child labour to provide data on child labour relevant to SDG indicator
No data available on effect of stoppage of child labour on enrolment of students or
parents’ awareness of disadvantages of child labour
No recent study/survey data available on specific industries and pocket areas where
concentration of hazardous child labour exists
Data gaps on child labour

23. Take necessary measures to conduct next Child Labour Survey 2020 following the 18th
ICLS recommendations on Child Labour Framework
Build comprehensive database of specific industries and pocket areas (potential source
of Hazardous Child Labour) through Child Labour Survey 2020
Develop database of households with Child labour through next Population and
Housing Census 2021 by including specific questions in the census questionnaire
Conduct Analytical Study based on Survey/Census microdata to identify
Determinants/Factors of child labour or to identify casual relationship of child labour
Measures may be taken to fill up data gaps